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tesseract/training/commontraining.cpp
Stefan Weil 9cbda9238e training: Fix typos in comments and strings 
All of them were found by codespell.

Signed-off-by: Stefan Weil <sw@weilnetz.de>
2016-02-05 11:12:39 +01:00

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// Copyright 2008 Google Inc. All Rights Reserved.
// Author: scharron@google.com (Samuel Charron)
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
// http://www.apache.org/licenses/LICENSE-2.0
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
#include "commontraining.h"
#include "allheaders.h"
#include "ccutil.h"
#include "classify.h"
#include "cluster.h"
#include "clusttool.h"
#include "efio.h"
#include "emalloc.h"
#include "featdefs.h"
#include "fontinfo.h"
#include "freelist.h"
#include "globals.h"
#include "intfeaturespace.h"
#include "mastertrainer.h"
#include "mf.h"
#include "ndminx.h"
#include "oldlist.h"
#include "params.h"
#include "shapetable.h"
#include "tessdatamanager.h"
#include "tessopt.h"
#include "tprintf.h"
#include "unicity_table.h"
#include <math.h>
using tesseract::CCUtil;
using tesseract::FontInfo;
using tesseract::IntFeatureSpace;
using tesseract::ParamUtils;
using tesseract::ShapeTable;
// Global Variables.
// global variable to hold configuration parameters to control clustering
// -M 0.625 -B 0.05 -I 1.0 -C 1e-6.
CLUSTERCONFIG Config = { elliptical, 0.625, 0.05, 1.0, 1e-6, 0 };
FEATURE_DEFS_STRUCT feature_defs;
CCUtil ccutil;
INT_PARAM_FLAG(debug_level, 0, "Level of Trainer debugging");
INT_PARAM_FLAG(load_images, 0, "Load images with tr files");
STRING_PARAM_FLAG(configfile, "", "File to load more configs from");
STRING_PARAM_FLAG(D, "", "Directory to write output files to");
STRING_PARAM_FLAG(F, "font_properties", "File listing font properties");
STRING_PARAM_FLAG(X, "", "File listing font xheights");
STRING_PARAM_FLAG(U, "unicharset", "File to load unicharset from");
STRING_PARAM_FLAG(O, "", "File to write unicharset to");
STRING_PARAM_FLAG(T, "", "File to load trainer from");
STRING_PARAM_FLAG(output_trainer, "", "File to write trainer to");
STRING_PARAM_FLAG(test_ch, "", "UTF8 test character string");
DOUBLE_PARAM_FLAG(clusterconfig_min_samples_fraction, Config.MinSamples,
"Min number of samples per proto as % of total");
DOUBLE_PARAM_FLAG(clusterconfig_max_illegal, Config.MaxIllegal,
"Max percentage of samples in a cluster which have more"
" than 1 feature in that cluster");
DOUBLE_PARAM_FLAG(clusterconfig_independence, Config.Independence,
"Desired independence between dimensions");
DOUBLE_PARAM_FLAG(clusterconfig_confidence, Config.Confidence,
"Desired confidence in prototypes created");
/**
* This routine parses the command line arguments that were
* passed to the program and ses them to set relevant
* training-related global parameters
*
* Globals:
* - Config current clustering parameters
* @param argc number of command line arguments to parse
* @param argv command line arguments
* @return none
* @note Exceptions: Illegal options terminate the program.
*/
void ParseArguments(int* argc, char ***argv) {
STRING usage;
if (*argc) {
usage += (*argv)[0];
}
usage += " [.tr files ...]";
tesseract::ParseCommandLineFlags(usage.c_str(), argc, argv, true);
// Record the index of the first non-flag argument to 1, since we set
// remove_flags to true when parsing the flags.
tessoptind = 1;
// Set some global values based on the flags.
Config.MinSamples =
MAX(0.0, MIN(1.0, double(FLAGS_clusterconfig_min_samples_fraction)));
Config.MaxIllegal =
MAX(0.0, MIN(1.0, double(FLAGS_clusterconfig_max_illegal)));
Config.Independence =
MAX(0.0, MIN(1.0, double(FLAGS_clusterconfig_independence)));
Config.Confidence =
MAX(0.0, MIN(1.0, double(FLAGS_clusterconfig_confidence)));
// Set additional parameters from config file if specified.
if (!FLAGS_configfile.empty()) {
tesseract::ParamUtils::ReadParamsFile(
FLAGS_configfile.c_str(),
tesseract::SET_PARAM_CONSTRAINT_NON_INIT_ONLY,
ccutil.params());
}
}
namespace tesseract {
// Helper loads shape table from the given file.
ShapeTable* LoadShapeTable(const STRING& file_prefix) {
ShapeTable* shape_table = NULL;
STRING shape_table_file = file_prefix;
shape_table_file += kShapeTableFileSuffix;
FILE* shape_fp = fopen(shape_table_file.string(), "rb");
if (shape_fp != NULL) {
shape_table = new ShapeTable;
if (!shape_table->DeSerialize(false, shape_fp)) {
delete shape_table;
shape_table = NULL;
tprintf("Error: Failed to read shape table %s\n",
shape_table_file.string());
} else {
int num_shapes = shape_table->NumShapes();
tprintf("Read shape table %s of %d shapes\n",
shape_table_file.string(), num_shapes);
}
fclose(shape_fp);
} else {
tprintf("Warning: No shape table file present: %s\n",
shape_table_file.string());
}
return shape_table;
}
// Helper to write the shape_table.
void WriteShapeTable(const STRING& file_prefix, const ShapeTable& shape_table) {
STRING shape_table_file = file_prefix;
shape_table_file += kShapeTableFileSuffix;
FILE* fp = fopen(shape_table_file.string(), "wb");
if (fp != NULL) {
if (!shape_table.Serialize(fp)) {
fprintf(stderr, "Error writing shape table: %s\n",
shape_table_file.string());
}
fclose(fp);
} else {
fprintf(stderr, "Error creating shape table: %s\n",
shape_table_file.string());
}
}
/**
* Creates a MasterTraininer and loads the training data into it:
* Initializes feature_defs and IntegerFX.
* Loads the shape_table if shape_table != NULL.
* Loads initial unicharset from -U command-line option.
* If FLAGS_T is set, loads the majority of data from there, else:
* - Loads font info from -F option.
* - Loads xheights from -X option.
* - Loads samples from .tr files in remaining command-line args.
* - Deletes outliers and computes canonical samples.
* - If FLAGS_output_trainer is set, saves the trainer for future use.
* Computes canonical and cloud features.
* If shape_table is not NULL, but failed to load, make a fake flat one,
* as shape clustering was not run.
*/
MasterTrainer* LoadTrainingData(int argc, const char* const * argv,
bool replication,
ShapeTable** shape_table,
STRING* file_prefix) {
InitFeatureDefs(&feature_defs);
InitIntegerFX();
*file_prefix = "";
if (!FLAGS_D.empty()) {
*file_prefix += FLAGS_D.c_str();
*file_prefix += "/";
}
// If we are shape clustering (NULL shape_table) or we successfully load
// a shape_table written by a previous shape clustering, then
// shape_analysis will be true, meaning that the MasterTrainer will replace
// some members of the unicharset with their fragments.
bool shape_analysis = false;
if (shape_table != NULL) {
*shape_table = LoadShapeTable(*file_prefix);
if (*shape_table != NULL)
shape_analysis = true;
} else {
shape_analysis = true;
}
MasterTrainer* trainer = new MasterTrainer(NM_CHAR_ANISOTROPIC,
shape_analysis,
replication,
FLAGS_debug_level);
IntFeatureSpace fs;
fs.Init(kBoostXYBuckets, kBoostXYBuckets, kBoostDirBuckets);
if (FLAGS_T.empty()) {
trainer->LoadUnicharset(FLAGS_U.c_str());
// Get basic font information from font_properties.
if (!FLAGS_F.empty()) {
if (!trainer->LoadFontInfo(FLAGS_F.c_str())) {
delete trainer;
return NULL;
}
}
if (!FLAGS_X.empty()) {
if (!trainer->LoadXHeights(FLAGS_X.c_str())) {
delete trainer;
return NULL;
}
}
trainer->SetFeatureSpace(fs);
const char* page_name;
// Load training data from .tr files on the command line.
while ((page_name = GetNextFilename(argc, argv)) != NULL) {
tprintf("Reading %s ...\n", page_name);
trainer->ReadTrainingSamples(page_name, feature_defs, false);
// If there is a file with [lang].[fontname].exp[num].fontinfo present,
// read font spacing information in to fontinfo_table.
int pagename_len = strlen(page_name);
char *fontinfo_file_name = new char[pagename_len + 7];
strncpy(fontinfo_file_name, page_name, pagename_len - 2); // remove "tr"
strcpy(fontinfo_file_name + pagename_len - 2, "fontinfo"); // +"fontinfo"
trainer->AddSpacingInfo(fontinfo_file_name);
delete[] fontinfo_file_name;
// Load the images into memory if required by the classifier.
if (FLAGS_load_images) {
STRING image_name = page_name;
// Chop off the tr and replace with tif. Extension must be tif!
image_name.truncate_at(image_name.length() - 2);
image_name += "tif";
trainer->LoadPageImages(image_name.string());
}
}
trainer->PostLoadCleanup();
// Write the master trainer if required.
if (!FLAGS_output_trainer.empty()) {
FILE* fp = fopen(FLAGS_output_trainer.c_str(), "wb");
if (fp == NULL) {
tprintf("Can't create saved trainer data!\n");
} else {
trainer->Serialize(fp);
fclose(fp);
}
}
} else {
bool success = false;
tprintf("Loading master trainer from file:%s\n",
FLAGS_T.c_str());
FILE* fp = fopen(FLAGS_T.c_str(), "rb");
if (fp == NULL) {
tprintf("Can't read file %s to initialize master trainer\n",
FLAGS_T.c_str());
} else {
success = trainer->DeSerialize(false, fp);
fclose(fp);
}
if (!success) {
tprintf("Deserialize of master trainer failed!\n");
delete trainer;
return NULL;
}
trainer->SetFeatureSpace(fs);
}
trainer->PreTrainingSetup();
if (!FLAGS_O.empty() &&
!trainer->unicharset().save_to_file(FLAGS_O.c_str())) {
fprintf(stderr, "Failed to save unicharset to file %s\n", FLAGS_O.c_str());
delete trainer;
return NULL;
}
if (shape_table != NULL) {
// If we previously failed to load a shapetable, then shape clustering
// wasn't run so make a flat one now.
if (*shape_table == NULL) {
*shape_table = new ShapeTable;
trainer->SetupFlatShapeTable(*shape_table);
tprintf("Flat shape table summary: %s\n",
(*shape_table)->SummaryStr().string());
}
(*shape_table)->set_unicharset(trainer->unicharset());
}
return trainer;
}
} // namespace tesseract.
/*---------------------------------------------------------------------------*/
/**
* This routine returns the next command line argument. If
* there are no remaining command line arguments, it returns
* NULL. This routine should only be called after all option
* arguments have been parsed and removed with ParseArguments.
*
* Globals:
* - tessoptind defined by tessopt sys call
* @return Next command line argument or NULL.
* @note Exceptions: none
* @note History: Fri Aug 18 09:34:12 1989, DSJ, Created.
*/
const char *GetNextFilename(int argc, const char* const * argv) {
if (tessoptind < argc)
return argv[tessoptind++];
else
return NULL;
} /* GetNextFilename */
/*---------------------------------------------------------------------------*/
/**
* This routine searches through a list of labeled lists to find
* a list with the specified label. If a matching labeled list
* cannot be found, NULL is returned.
* @param List list to search
* @param Label label to search for
* @return Labeled list with the specified Label or NULL.
* @note Globals: none
* @note Exceptions: none
* @note History: Fri Aug 18 15:57:41 1989, DSJ, Created.
*/
LABELEDLIST FindList (
LIST List,
char *Label)
{
LABELEDLIST LabeledList;
iterate (List)
{
LabeledList = (LABELEDLIST) first_node (List);
if (strcmp (LabeledList->Label, Label) == 0)
return (LabeledList);
}
return (NULL);
} /* FindList */
/*---------------------------------------------------------------------------*/
/**
* This routine allocates a new, empty labeled list and gives
* it the specified label.
* @param Label label for new list
* @return New, empty labeled list.
* @note Globals: none
* @note Exceptions: none
* @note History: Fri Aug 18 16:08:46 1989, DSJ, Created.
*/
LABELEDLIST NewLabeledList (
const char *Label)
{
LABELEDLIST LabeledList;
LabeledList = (LABELEDLIST) Emalloc (sizeof (LABELEDLISTNODE));
LabeledList->Label = (char*)Emalloc (strlen (Label)+1);
strcpy (LabeledList->Label, Label);
LabeledList->List = NIL_LIST;
LabeledList->SampleCount = 0;
LabeledList->font_sample_count = 0;
return (LabeledList);
} /* NewLabeledList */
/*---------------------------------------------------------------------------*/
// TODO(rays) This is now used only by cntraining. Convert cntraining to use
// the new method or get rid of it entirely.
/**
* This routine reads training samples from a file and
* places them into a data structure which organizes the
* samples by FontName and CharName. It then returns this
* data structure.
* @param file open text file to read samples from
* @param feature_defs
* @param feature_name
* @param max_samples
* @param unicharset
* @param training_samples
* @return none
* @note Globals: none
* @note Exceptions: none
* @note History:
* - Fri Aug 18 13:11:39 1989, DSJ, Created.
* - Tue May 17 1998 simplifications to structure, illiminated
* font, and feature specification levels of structure.
*/
void ReadTrainingSamples(const FEATURE_DEFS_STRUCT& feature_defs,
const char *feature_name, int max_samples,
UNICHARSET* unicharset,
FILE* file, LIST* training_samples) {
char buffer[2048];
char unichar[UNICHAR_LEN + 1];
LABELEDLIST char_sample;
FEATURE_SET feature_samples;
CHAR_DESC char_desc;
int i;
int feature_type = ShortNameToFeatureType(feature_defs, feature_name);
// Zero out the font_sample_count for all the classes.
LIST it = *training_samples;
iterate(it) {
char_sample = reinterpret_cast<LABELEDLIST>(first_node(it));
char_sample->font_sample_count = 0;
}
while (fgets(buffer, 2048, file) != NULL) {
if (buffer[0] == '\n')
continue;
sscanf(buffer, "%*s %s", unichar);
if (unicharset != NULL && !unicharset->contains_unichar(unichar)) {
unicharset->unichar_insert(unichar);
if (unicharset->size() > MAX_NUM_CLASSES) {
tprintf("Error: Size of unicharset in training is "
"greater than MAX_NUM_CLASSES\n");
exit(1);
}
}
char_sample = FindList(*training_samples, unichar);
if (char_sample == NULL) {
char_sample = NewLabeledList(unichar);
*training_samples = push(*training_samples, char_sample);
}
char_desc = ReadCharDescription(feature_defs, file);
feature_samples = char_desc->FeatureSets[feature_type];
if (char_sample->font_sample_count < max_samples || max_samples <= 0) {
char_sample->List = push(char_sample->List, feature_samples);
char_sample->SampleCount++;
char_sample->font_sample_count++;
} else {
FreeFeatureSet(feature_samples);
}
for (i = 0; i < char_desc->NumFeatureSets; i++) {
if (feature_type != i)
FreeFeatureSet(char_desc->FeatureSets[i]);
}
free(char_desc);
}
} // ReadTrainingSamples
/*---------------------------------------------------------------------------*/
/**
* This routine deallocates all of the space allocated to
* the specified list of training samples.
* @param CharList list of all fonts in document
* @return none
* @note Globals: none
* @note Exceptions: none
* @note History: Fri Aug 18 17:44:27 1989, DSJ, Created.
*/
void FreeTrainingSamples(LIST CharList) {
LABELEDLIST char_sample;
FEATURE_SET FeatureSet;
LIST FeatureList;
iterate(CharList) { /* iterate through all of the fonts */
char_sample = (LABELEDLIST) first_node(CharList);
FeatureList = char_sample->List;
iterate(FeatureList) { /* iterate through all of the classes */
FeatureSet = (FEATURE_SET) first_node(FeatureList);
FreeFeatureSet(FeatureSet);
}
FreeLabeledList(char_sample);
}
destroy(CharList);
} /* FreeTrainingSamples */
/*---------------------------------------------------------------------------*/
/**
* This routine deallocates all of the memory consumed by
* a labeled list. It does not free any memory which may be
* consumed by the items in the list.
* @param LabeledList labeled list to be freed
* @note Globals: none
* @return none
* @note Exceptions: none
* @note History: Fri Aug 18 17:52:45 1989, DSJ, Created.
*/
void FreeLabeledList(LABELEDLIST LabeledList) {
destroy(LabeledList->List);
free(LabeledList->Label);
free(LabeledList);
} /* FreeLabeledList */
/*---------------------------------------------------------------------------*/
/**
* This routine reads samples from a LABELEDLIST and enters
* those samples into a clusterer data structure. This
* data structure is then returned to the caller.
* @param char_sample: LABELEDLIST that holds all the feature information for a
* @param FeatureDefs
* @param program_feature_type
* given character.
* @return Pointer to new clusterer data structure.
* @note Globals: None
* @note Exceptions: None
* @note History: 8/16/89, DSJ, Created.
*/
CLUSTERER *SetUpForClustering(const FEATURE_DEFS_STRUCT &FeatureDefs,
LABELEDLIST char_sample,
const char* program_feature_type) {
uinT16 N;
int i, j;
FLOAT32 *Sample = NULL;
CLUSTERER *Clusterer;
inT32 CharID;
LIST FeatureList = NULL;
FEATURE_SET FeatureSet = NULL;
int desc_index = ShortNameToFeatureType(FeatureDefs, program_feature_type);
N = FeatureDefs.FeatureDesc[desc_index]->NumParams;
Clusterer = MakeClusterer(N, FeatureDefs.FeatureDesc[desc_index]->ParamDesc);
FeatureList = char_sample->List;
CharID = 0;
iterate(FeatureList) {
FeatureSet = (FEATURE_SET) first_node(FeatureList);
for (i = 0; i < FeatureSet->MaxNumFeatures; i++) {
if (Sample == NULL)
Sample = (FLOAT32 *)Emalloc(N * sizeof(FLOAT32));
for (j = 0; j < N; j++)
Sample[j] = FeatureSet->Features[i]->Params[j];
MakeSample (Clusterer, Sample, CharID);
}
CharID++;
}
if ( Sample != NULL ) free( Sample );
return( Clusterer );
} /* SetUpForClustering */
/*------------------------------------------------------------------------*/
void MergeInsignificantProtos(LIST ProtoList, const char* label,
CLUSTERER *Clusterer, CLUSTERCONFIG *Config) {
PROTOTYPE *Prototype;
bool debug = strcmp(FLAGS_test_ch.c_str(), label) == 0;
LIST pProtoList = ProtoList;
iterate(pProtoList) {
Prototype = (PROTOTYPE *) first_node (pProtoList);
if (Prototype->Significant || Prototype->Merged)
continue;
FLOAT32 best_dist = 0.125;
PROTOTYPE* best_match = NULL;
// Find the nearest alive prototype.
LIST list_it = ProtoList;
iterate(list_it) {
PROTOTYPE* test_p = (PROTOTYPE *) first_node (list_it);
if (test_p != Prototype && !test_p->Merged) {
FLOAT32 dist = ComputeDistance(Clusterer->SampleSize,
Clusterer->ParamDesc,
Prototype->Mean, test_p->Mean);
if (dist < best_dist) {
best_match = test_p;
best_dist = dist;
}
}
}
if (best_match != NULL && !best_match->Significant) {
if (debug)
tprintf("Merging red clusters (%d+%d) at %g,%g and %g,%g\n",
best_match->NumSamples, Prototype->NumSamples,
best_match->Mean[0], best_match->Mean[1],
Prototype->Mean[0], Prototype->Mean[1]);
best_match->NumSamples = MergeClusters(Clusterer->SampleSize,
Clusterer->ParamDesc,
best_match->NumSamples,
Prototype->NumSamples,
best_match->Mean,
best_match->Mean, Prototype->Mean);
Prototype->NumSamples = 0;
Prototype->Merged = 1;
} else if (best_match != NULL) {
if (debug)
tprintf("Red proto at %g,%g matched a green one at %g,%g\n",
Prototype->Mean[0], Prototype->Mean[1],
best_match->Mean[0], best_match->Mean[1]);
Prototype->Merged = 1;
}
}
// Mark significant those that now have enough samples.
int min_samples = (inT32) (Config->MinSamples * Clusterer->NumChar);
pProtoList = ProtoList;
iterate(pProtoList) {
Prototype = (PROTOTYPE *) first_node (pProtoList);
// Process insignificant protos that do not match a green one
if (!Prototype->Significant && Prototype->NumSamples >= min_samples &&
!Prototype->Merged) {
if (debug)
tprintf("Red proto at %g,%g becoming green\n",
Prototype->Mean[0], Prototype->Mean[1]);
Prototype->Significant = true;
}
}
} /* MergeInsignificantProtos */
/*-----------------------------------------------------------------------------*/
void CleanUpUnusedData(
LIST ProtoList)
{
PROTOTYPE* Prototype;
iterate(ProtoList)
{
Prototype = (PROTOTYPE *) first_node (ProtoList);
if(Prototype->Variance.Elliptical != NULL)
{
memfree(Prototype->Variance.Elliptical);
Prototype->Variance.Elliptical = NULL;
}
if(Prototype->Magnitude.Elliptical != NULL)
{
memfree(Prototype->Magnitude.Elliptical);
Prototype->Magnitude.Elliptical = NULL;
}
if(Prototype->Weight.Elliptical != NULL)
{
memfree(Prototype->Weight.Elliptical);
Prototype->Weight.Elliptical = NULL;
}
}
}
/*------------------------------------------------------------------------*/
LIST RemoveInsignificantProtos(
LIST ProtoList,
BOOL8 KeepSigProtos,
BOOL8 KeepInsigProtos,
int N)
{
LIST NewProtoList = NIL_LIST;
LIST pProtoList;
PROTOTYPE* Proto;
PROTOTYPE* NewProto;
int i;
pProtoList = ProtoList;
iterate(pProtoList)
{
Proto = (PROTOTYPE *) first_node (pProtoList);
if ((Proto->Significant && KeepSigProtos) ||
(!Proto->Significant && KeepInsigProtos))
{
NewProto = (PROTOTYPE *)Emalloc(sizeof(PROTOTYPE));
NewProto->Mean = (FLOAT32 *)Emalloc(N * sizeof(FLOAT32));
NewProto->Significant = Proto->Significant;
NewProto->Style = Proto->Style;
NewProto->NumSamples = Proto->NumSamples;
NewProto->Cluster = NULL;
NewProto->Distrib = NULL;
for (i=0; i < N; i++)
NewProto->Mean[i] = Proto->Mean[i];
if (Proto->Variance.Elliptical != NULL)
{
NewProto->Variance.Elliptical = (FLOAT32 *)Emalloc(N * sizeof(FLOAT32));
for (i=0; i < N; i++)
NewProto->Variance.Elliptical[i] = Proto->Variance.Elliptical[i];
}
else
NewProto->Variance.Elliptical = NULL;
//---------------------------------------------
if (Proto->Magnitude.Elliptical != NULL)
{
NewProto->Magnitude.Elliptical = (FLOAT32 *)Emalloc(N * sizeof(FLOAT32));
for (i=0; i < N; i++)
NewProto->Magnitude.Elliptical[i] = Proto->Magnitude.Elliptical[i];
}
else
NewProto->Magnitude.Elliptical = NULL;
//------------------------------------------------
if (Proto->Weight.Elliptical != NULL)
{
NewProto->Weight.Elliptical = (FLOAT32 *)Emalloc(N * sizeof(FLOAT32));
for (i=0; i < N; i++)
NewProto->Weight.Elliptical[i] = Proto->Weight.Elliptical[i];
}
else
NewProto->Weight.Elliptical = NULL;
NewProto->TotalMagnitude = Proto->TotalMagnitude;
NewProto->LogMagnitude = Proto->LogMagnitude;
NewProtoList = push_last(NewProtoList, NewProto);
}
}
FreeProtoList(&ProtoList);
return (NewProtoList);
} /* RemoveInsignificantProtos */
/*----------------------------------------------------------------------------*/
MERGE_CLASS FindClass (
LIST List,
const char *Label)
{
MERGE_CLASS MergeClass;
iterate (List)
{
MergeClass = (MERGE_CLASS) first_node (List);
if (strcmp (MergeClass->Label, Label) == 0)
return (MergeClass);
}
return (NULL);
} /* FindClass */
/*---------------------------------------------------------------------------*/
MERGE_CLASS NewLabeledClass (
const char *Label)
{
MERGE_CLASS MergeClass;
MergeClass = new MERGE_CLASS_NODE;
MergeClass->Label = (char*)Emalloc (strlen (Label)+1);
strcpy (MergeClass->Label, Label);
MergeClass->Class = NewClass (MAX_NUM_PROTOS, MAX_NUM_CONFIGS);
return (MergeClass);
} /* NewLabeledClass */
/*-----------------------------------------------------------------------------*/
/**
* This routine deallocates all of the space allocated to
* the specified list of training samples.
* @param ClassList list of all fonts in document
* @return none
* @note Globals: none
* @note Exceptions: none
* @note History: Fri Aug 18 17:44:27 1989, DSJ, Created.
*/
void FreeLabeledClassList (
LIST ClassList)
{
MERGE_CLASS MergeClass;
iterate (ClassList) /* iterate through all of the fonts */
{
MergeClass = (MERGE_CLASS) first_node (ClassList);
free (MergeClass->Label);
FreeClass(MergeClass->Class);
delete MergeClass;
}
destroy (ClassList);
} /* FreeLabeledClassList */
/* SetUpForFloat2Int */
CLASS_STRUCT* SetUpForFloat2Int(const UNICHARSET& unicharset,
LIST LabeledClassList) {
MERGE_CLASS MergeClass;
CLASS_TYPE Class;
int NumProtos;
int NumConfigs;
int NumWords;
int i, j;
float Values[3];
PROTO NewProto;
PROTO OldProto;
BIT_VECTOR NewConfig;
BIT_VECTOR OldConfig;
// printf("Float2Int ...\n");
CLASS_STRUCT* float_classes = new CLASS_STRUCT[unicharset.size()];
iterate(LabeledClassList)
{
UnicityTableEqEq<int> font_set;
MergeClass = (MERGE_CLASS) first_node (LabeledClassList);
Class = &float_classes[unicharset.unichar_to_id(MergeClass->Label)];
NumProtos = MergeClass->Class->NumProtos;
NumConfigs = MergeClass->Class->NumConfigs;
font_set.move(&MergeClass->Class->font_set);
Class->NumProtos = NumProtos;
Class->MaxNumProtos = NumProtos;
Class->Prototypes = (PROTO) Emalloc (sizeof(PROTO_STRUCT) * NumProtos);
for(i=0; i < NumProtos; i++)
{
NewProto = ProtoIn(Class, i);
OldProto = ProtoIn(MergeClass->Class, i);
Values[0] = OldProto->X;
Values[1] = OldProto->Y;
Values[2] = OldProto->Angle;
Normalize(Values);
NewProto->X = OldProto->X;
NewProto->Y = OldProto->Y;
NewProto->Length = OldProto->Length;
NewProto->Angle = OldProto->Angle;
NewProto->A = Values[0];
NewProto->B = Values[1];
NewProto->C = Values[2];
}
Class->NumConfigs = NumConfigs;
Class->MaxNumConfigs = NumConfigs;
Class->font_set.move(&font_set);
Class->Configurations = (BIT_VECTOR*) Emalloc (sizeof(BIT_VECTOR) * NumConfigs);
NumWords = WordsInVectorOfSize(NumProtos);
for(i=0; i < NumConfigs; i++)
{
NewConfig = NewBitVector(NumProtos);
OldConfig = MergeClass->Class->Configurations[i];
for(j=0; j < NumWords; j++)
NewConfig[j] = OldConfig[j];
Class->Configurations[i] = NewConfig;
}
}
return float_classes;
} // SetUpForFloat2Int
/*--------------------------------------------------------------------------*/
void Normalize (
float *Values)
{
register float Slope;
register float Intercept;
register float Normalizer;
Slope = tan (Values [2] * 2 * PI);
Intercept = Values [1] - Slope * Values [0];
Normalizer = 1 / sqrt (Slope * Slope + 1.0);
Values [0] = Slope * Normalizer;
Values [1] = - Normalizer;
Values [2] = Intercept * Normalizer;
} // Normalize
/*-------------------------------------------------------------------------*/
void FreeNormProtoList (
LIST CharList)
{
LABELEDLIST char_sample;
iterate (CharList) /* iterate through all of the fonts */
{
char_sample = (LABELEDLIST) first_node (CharList);
FreeLabeledList (char_sample);
}
destroy (CharList);
} // FreeNormProtoList
/*---------------------------------------------------------------------------*/
void AddToNormProtosList(
LIST* NormProtoList,
LIST ProtoList,
char* CharName)
{
PROTOTYPE* Proto;
LABELEDLIST LabeledProtoList;
LabeledProtoList = NewLabeledList(CharName);
iterate(ProtoList)
{
Proto = (PROTOTYPE *) first_node (ProtoList);
LabeledProtoList->List = push(LabeledProtoList->List, Proto);
}
*NormProtoList = push(*NormProtoList, LabeledProtoList);
}
/*---------------------------------------------------------------------------*/
int NumberOfProtos(
LIST ProtoList,
BOOL8 CountSigProtos,
BOOL8 CountInsigProtos)
{
int N = 0;
PROTOTYPE *Proto;
iterate(ProtoList)
{
Proto = (PROTOTYPE *) first_node ( ProtoList );
if (( Proto->Significant && CountSigProtos ) ||
( ! Proto->Significant && CountInsigProtos ) )
N++;
}
return(N);
}
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